A systematic literature review of deep learning neural network for time series air quality forecasting
Rapid progress of industrial development, urbanization and traffic has caused air quality
reduction that negatively affects human health and environmental sustainability, especially …
reduction that negatively affects human health and environmental sustainability, especially …
Machine learning algorithms to forecast air quality: a survey
Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is
important to develop forecasting mechanisms that can be used by the authorities, so that …
important to develop forecasting mechanisms that can be used by the authorities, so that …
MFRFNN: Multi-functional recurrent fuzzy neural network for chaotic time series prediction
H Nasiri, MM Ebadzadeh - Neurocomputing, 2022 - Elsevier
Chaotic time series prediction, a challenging research topic in dynamic system modeling,
has drawn great attention from researchers around the world. In recent years extensive …
has drawn great attention from researchers around the world. In recent years extensive …
Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis
G Li, L Chen, J Liu, X Fang - Energy, 2023 - Elsevier
Timely and accurate fault diagnosis (FD) in building energy systems (BESs) can promote
energy efficiency and sustainable development. Especially the heating, ventilating, and air …
energy efficiency and sustainable development. Especially the heating, ventilating, and air …
[HTML][HTML] A blockchain and IoT-based lightweight framework for enabling information transparency in supply chain finance
Abstract Supply Chain Finance (SCF) refers to the financial service in which banks rely on
core enterprises to manage the capital flow and logistics of upstream and downstream …
core enterprises to manage the capital flow and logistics of upstream and downstream …
Forecasting turning points in stock price by applying a novel hybrid CNN-LSTM-ResNet model fed by 2D segmented images
This paper aims to forecast stock price Turning Points (TPs) with a developed hybrid
Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. To this …
Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. To this …
Deep Learning Enhanced Solar Energy Forecasting with AI‐Driven IoT
Short‐term photovoltaic (PV) energy generation forecasting models are important, stabilizing
the power integration between the PV and the smart grid for artificial intelligence‐(AI‐) …
the power integration between the PV and the smart grid for artificial intelligence‐(AI‐) …
Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart Cities
V Papastefanopoulos, P Linardatos… - Smart Cities, 2023 - mdpi.com
Smart cities are urban areas that utilize digital solutions to enhance the efficiency of
conventional networks and services for sustainable growth, optimized resource …
conventional networks and services for sustainable growth, optimized resource …
A New Hybrid Forecasting Model Based on Dual Series Decomposition with Long‐Term Short‐Term Memory
In recent years, ozone (O3) has gradually become the primary pollutant plaguing urban air
quality. Accurate and efficient ozone prediction is of great significance to the prevention and …
quality. Accurate and efficient ozone prediction is of great significance to the prevention and …
A novel deep learning approach for anomaly detection of time series data
Z Ji, J Gong, J Feng - Scientific Programming, 2021 - Wiley Online Library
Anomalies in time series, also called “discord,” are the abnormal subsequences. The
occurrence of anomalies in time series may indicate that some faults or disease will occur …
occurrence of anomalies in time series may indicate that some faults or disease will occur …